Explore the recent global developments with R

Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.

Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.

Look at the data

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

unique(gapminder$year)
##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  geom_text(aes(label=country),hjust=1, vjust=2)

We see an interesting spread with an outlier to the right. Answer the following questions, please:

Q1. Why does it make sense to have a log10 scale on x axis? As the gdpPercap (what ever that stands for) Grows logaritmicly and viualisations therefore become clearer when we scale them

Q2. What country is the richest in 1952 (far right on x axis)?

filter(gapminder, year == 1952)  %>%
arrange(gdpPercap) %>% 
tail()
## # A tibble: 6 x 6
##   country       continent  year lifeExp       pop gdpPercap
##   <fct>         <fct>     <int>   <dbl>     <int>     <dbl>
## 1 Norway        Europe     1952    72.7   3327728    10095.
## 2 New Zealand   Oceania    1952    69.4   1994794    10557.
## 3 Canada        Americas   1952    68.8  14785584    11367.
## 4 United States Americas   1952    68.4 157553000    13990.
## 5 Switzerland   Europe     1952    69.6   4815000    14734.
## 6 Kuwait        Asia       1952    55.6    160000   108382.

A country with a very small population size - Kuwait

You can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + 
  ylab("Life Expectancy") +
  xlab("GDP per capita") +
  geom_text(aes(label=country),hjust=1, vjust=2)

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Q3. Can you differentiate the continents by color and fix the axis labels? I tried, but as there is no discribtion of what the variable “gdpPercap” is, it’s hard to know what is stands for, but I’m just guessing here

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() +
  scale_x_log10() + 
  ylab("Life Expectancy") +
  xlab("GDP per capita") +
  geom_text(aes(label=country),hjust=1, vjust=2)

Q4. What are the five richest countries in the world in 2007?

filter(gapminder, year == 2007)  %>%
arrange(gdpPercap) %>% 
tail()
## # A tibble: 6 x 6
##   country          continent  year lifeExp       pop gdpPercap
##   <fct>            <fct>     <int>   <dbl>     <int>     <dbl>
## 1 Hong Kong, China Asia       2007    82.2   6980412    39725.
## 2 Ireland          Europe     2007    78.9   4109086    40676.
## 3 United States    Americas   2007    78.2 301139947    42952.
## 4 Singapore        Asia       2007    80.0   4553009    47143.
## 5 Kuwait           Asia       2007    77.6   2505559    47307.
## 6 Norway           Europe     2007    80.2   4627926    49357.

Norway, Kuwait, Singapore, the US, Ireland

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. And there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the viz inside an html file.

anim1 <- anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)
anim1

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year)
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Q5 Can you add a title to one or both of the animations above that will change in sync with the animation? [hint: search labeling for transition_states() and transition_time() functions respectively] See chunk below

Q6 Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.[hint:search disabling scientific notation]

options(scipen=10000) #should remove scientific notation and answer Q6 - i think scipen = just has to be a high enough number

anim1_1 <- anim1 + labs(x = "GDP per capita", y = "Life expectancy") + transition_time(year) +
labs(title = "Year: {frame_time}")
anim1_1

anim2_1 <- anim2 +   labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  ease_aes('linear')
anim2_1

Q7 Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]

What has changed between my mom and I was born (1960 vs 1998) –> closets values in the data is 1962 vs1997

p_60 <- ggplot(subset(gapminder, year == 1962), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() +
  scale_x_log10() + 
  ylab("Life Expectancy") +
  xlab("GDP per capita") 

p_98 <- ggplot(subset(gapminder, year == 1997), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() +
  scale_x_log10() + 
  ylab("Life Expectancy") +
  xlab("GDP per capita") 

plot_grid(p_60, p_98, labels = c('1962', '1997'))

Overall, all countires have improved health and wealth over the time period. The plot suggests that Asia have inproved in both health and wealth together with the american countries. Also, some areas of africa seems to have increased more than average as well.